Real-time Responses System in Improving Services at Arun Lhokseumawe Hospital Using Natural Language Processing Methods

Case Study at Arun Hospital to Increase Service Speed

Abstract

Abstract

Based on information from the Central Statistics Agency, the population of of Lhokseumawe City reached 191,396 people in 2022, which requires Rumah Sakit Arun Lhokseumawe to enhance its healthcare services to the community. The increase in population presents challenges for the hospital in addressing patients with health issues. Therefore, the author proposes the development of a "Real-Time Responses" system to improve services at Rumah Sakit Arun by utilizing Natural Language Processing (NLP) methods. This system aims to assist patients and the public in seeking information related to health and services available at the hospital. The approach used in developing the chatbot Long Short Term Memory (LSTM) is a component of NLP technology. NLP, in turn, is a field of artificial intelligence (AI) that allows computers to comprehend human text and speech. This technology combines linguistic computation with statistical models, machine learning, and deep learning, allowing computers to process human language in text or voice form and fully comprehend the meaning and sentiment of the writer or speaker. By applying this technology, it becomes expected that communication between the hospital and the community will become more effective, and access to health information can be obtained more quickly, supporting the enhancement of healthcare service quality in Lhokseumawe.

Keywords: chatbot, NLP, LSTM, Arun Lhokseumawe, information